Macular GCIPL Thickness Map Prediction via Time-Aware Convolutional LSTM

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Macular GCIPL Thickness Map Prediction via Time-Aware Convolutional LSTM


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Macular GCIPL Thickness Map Prediction via Time-Aware Convolutional LSTM

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Macular ganglion cell inner plexiform layer (GCIPL) thickness is an important biomarker for clinical managements of glaucoma. Clinical analysis of GCIPL progression uses averaged thickness only, which easily washes out small changes and reveals no spatial patterns. This is the first work to predict the 2D GCIPL thickness map. We propose a novel Time-aware Convolutional Long Short-Term Memory (TC-LSTM) unit to decompose memories into the short-term and long-term memories and exploit time intervals to penalize the short-term memory. TC-LSTM unit is incorporated into an auto-encoder-decoder so that the end-to-end model can handle irregular sampling intervals of longitudinal GCIPL thickness map sequences and capture both spatial and temporal correlations. Experiments show the superiority of the proposed model over the traditional method.
Macular ganglion cell inner plexiform layer (GCIPL) thickness is an important biomarker for clinical managements of glaucoma. Clinical analysis of GCIPL progression uses averaged thickness only, which easily washes out small changes and reveals no spatial patterns. This is the first work to predict the 2D GCIPL thickness map. We propose a novel Time-aware Convolutional Long Short-Term Memory (TC-LSTM) unit to decompose memories into the short-term and long-term memories and exploit time intervals to penalize the short-term memory. TC-LSTM unit is incorporated into an auto-encoder-decoder so that the end-to-end model can handle irregular sampling intervals of longitudinal GCIPL thickness map sequences and capture both spatial and temporal correlations. Experiments show the superiority of the proposed model over the traditional method.